Correlation vs. Causation: Measuring True Impact with Propensity Score Matching

Propensity Score Matching (PSM) is a statistical technique for measuring the causal impact of an action when a controlled experiment is not feasible. It addresses selection bias by creating "statistical twins," matching individuals who received a treatment with similar individuals who did not based on observed characteristics. The process involves using a logistic regression model to calculate a propensity score, which represents the probability of receiving the treatment. A step-by-step Python implementation demonstrates using the Nearest Neighbors algorithm to find these matches and then evaluating the balance between the new groups with metrics like the Standardized Mean Difference (SMD) to ensure a fair comparison.